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ai_data_preparation [2025/05/25 17:58] – [Future Enhancements] eagleeyenebulaai_data_preparation [2025/05/25 18:13] (current) – [Future Enhancements] eagleeyenebula
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 ====== AI Data Preparation ====== ====== AI Data Preparation ======
-* **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:+**[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:
 ===== Overview ===== ===== Overview =====
 The **AI Data Preparation** module provides a robust framework for preparing raw datasets for further analysis, feature engineering, and machine learning workflows. It automates common tasks such as cleaning, normalization, and feature preparation, ensuring that data is clean, consistent, and ready for downstream tasks. The **AI Data Preparation** module provides a robust framework for preparing raw datasets for further analysis, feature engineering, and machine learning workflows. It automates common tasks such as cleaning, normalization, and feature preparation, ensuring that data is clean, consistent, and ready for downstream tasks.
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   * **Data Cleaning:**   * **Data Cleaning:**
-    * Removes `None` or invalid entries from raw datasets.+    * Removes **None** or invalid entries from raw datasets.
     * Provides extensibility to define custom cleaning logic.     * Provides extensibility to define custom cleaning logic.
  
   * **Data Normalization:**   * **Data Normalization:**
-    * Scales numerical data to a standard range (e.g., 0 to 1) with Min-Max normalization, improving compatibility with machine learning algorithms.+    * Scales numerical data to a standard range (e.g., **0** to **1**) with Min-Max normalization, improving compatibility with machine learning algorithms.
  
   * **Error Handling and Logging:**   * **Error Handling and Logging:**
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 ===== Best Practices ===== ===== Best Practices =====
 1. **Analyze Data Before Preparation:** 1. **Analyze Data Before Preparation:**
-   - Inspect datasets for unique issues (e.g., outliers) before applying generalized cleaning rules.+   - Inspect datasets for unique issues (e.g., **outliers**) before applying generalized cleaning rules.
  
 2. **Normalize for ML Algorithms:** 2. **Normalize for ML Algorithms:**
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 The **DataPreparation** module can be extended for advanced preprocessing tasks: The **DataPreparation** module can be extended for advanced preprocessing tasks:
   * **Custom Outlier Removal:**   * **Custom Outlier Removal:**
-    - Add logic to discard outliers based on statistical bounds (e.g., Z-scores, IQR).+    - Add logic to discard outliers based on statistical bounds (e.g., **Z-scores, IQR**).
   * **Feature Engineering:**   * **Feature Engineering:**
     - Extract derived metrics from datasets, such as mean, variance, or ratios.     - Extract derived metrics from datasets, such as mean, variance, or ratios.
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 - **Advanced Scaling Options**   - **Advanced Scaling Options**  
-  Support additional normalization techniques such as Z-score scaling or logarithmic transformations.+  Support additional normalization techniques such as **Z-score** scaling or logarithmic transformations.
  
 - **Distributed Processing**   - **Distributed Processing**  
ai_data_preparation.1748195894.txt.gz · Last modified: 2025/05/25 17:58 by eagleeyenebula